| 1 |
Multi-Agent Feedback Motion Planning using Probably Approximately Correct Nonlinear Model Predictive Control |
提出基于PAC-NMPC的多智能体反馈运动规划，解决动态环境下编队控制与避障问题 |
model predictive control motion planning |
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| 2 |
Learning to Hop for a Single-Legged Robot with Parallel Mechanism |
提出一种基于强化学习的单腿并联机器人跳跃控制方法，解决仿真和实际部署难题。 |
legged robot sim-to-real reinforcement learning |
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| 3 |
Evaluating Efficiency and Engagement in Scripted and LLM-Enhanced Human-Robot Interactions |
对比脚本式与LLM增强人机交互，评估效率与参与度 |
manipulation large language model |
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| 4 |
ImageInThat: Manipulating Images to Convey User Instructions to Robots |
提出ImageInThat，通过图像操作向机器人传达指令，提升厨房操作任务效率。 |
manipulation foundation model |
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